Search our Publications
Latest Publications
Timo Friedrich, Stefan Menzel, Sebastian Schmitt , "Rapid Creation Of Vehicle Line-ups By Eigenspace Projections For Style Transfer ", Design 2020 - 16th international design conference, no. 1, pp. 867-876, 2020.
AbstractIn product development, an automated generation of shape variations enables a rapid assessment of potentially appealing future design directions. In the present paper, we propose a framework for computing a product line-up of 3D automotive body shapes based on spectral methods for mesh processing. Our proposed method utilizes the visual features extracted as style from a designed car model and projects them onto differently shaped car models, e.g...
Download Bibtex file
Nima Nabizadeh, Martin Ernst Heckmann, Dorothea Kolossa , "Target-aware Prediction of Tool Usage in Sequential Repair Tasks", Machine Learning, Optimization, and Data Science, vol. 2, issue 1, pp. 869–880, 2020.
AbstractMany real-world tasks have a sequential nature comprising several steps, and in a complex repair task, each step might involve a different tool. Learning the sequential pattern of tool usage would be helpful for various assistance scenarios, e.g.~allowing a contextualized assistant to predict the next required tool in an unseen task. In this work, we examine the potential of this idea on an example of sequential tasks: repairing electronic device...
Download Bibtex file
Karam Sai Krishna Kaushik , "SFA based Self Localization using Natural Landmarks", UAS Frankfurt, 2020.
AbstractThe ability of a mobile robot to locate itself in an unknown environment is an essential criterion to implement any intelligent behavior. As cameras are becoming extremely inexpensive these days, the vision-based localization is one of the most feasible options that one can choose to build a mobile robotic system. The fundamental aspect of the robot’s localization is the spatial representation which could be extracted using the unsupervised Slo...
Download Bibtex file Per Mail Request
Benedict Flade, Axel Koppert, Gorka Vélez Isasmendi, Anweshan Das, David Bétaille, Gijs Dubbelman, Oihana Otaegui, Julian Eggert , "Vision-Enhanced Low-Cost Localization in Crowdsourced Maps", IEEE Intelligent Transportation Systems Magazine, vol. 12, no. 3, pp. 70 - 80, 2020.
AbstractLane-level localization of vehicles with low-cost sensors is a challenging task. In situations in which Global Navigation Satellite Systems (GNSS) suffer from weak observation geometry or the influence of reflected signals, the fusion of heterogeneous information presents a suitable approach for improving the localization accuracy. We propose a solution based on a monocular front-facing camera, a low-cost inertial measurement unit (IMU) and a si...
Download Bibtex file
Simon Manschitz , "Learning, generating and adapting wave gestures for expressive human-robot interaction", ACM/IEEE International Conference on Human Robot Interaction, 2020.
AbstractWhile many humanoid robots can perform basic wave gestures, these gestures are usually hard-coded behaviors. Consequently, the gesture looks rather stiff since there is no variance in the execution of the movement. This study proposes a novel imitation learning approach for the stochastic generation of human-like rhythmic wave gestures and their modulation for effective nonverbal communication through a probabilistic formulation using joint angle...
Download Bibtex file Per Mail Request
Simon Manschitz, Michael Gienger, Jens Kober, Jan Peters , "Learning Sequential Force Interaction Skills", Robotics, vol. 9, no. 2, 2020.
AbstractLearning skills from kinesthetic demonstrations is a promising way of minimizing the gap between human manipulation abilities and those of robots. We propose an approach to learn sequential force interaction skills from such demonstrations. The demonstrations are decomposed into a set of movement primitives by inferring the underlying sequential structure of the task. The decomposition is based on a novel probability distribution which we call Di...
Download Bibtex file
Manuel Dietrich , "Understanding Autonomous Driving as Institutional Activity: Opening New Ways to React to Discriminatory Concerns in Autonomous Driving", Culturally Sustainable Social Robotics: Proceedings of Robophilosophy 2020 , vol. 335, pp. 373 - 383, 2020.
AbstractWe will elaborate why it is appropriate to be concerned about structural discrimination induced by future autonomous vehicles – not only in the context of crash-optimization. In order to understand discrimination effects and potential strategies to reduce them, a novel conceptual perspective on how to frame autonomous driving is introduced. According to that, autonomous driving is considered as an institutional activity. This perspective enables ...
Download Bibtex file
Manuel Mühlig, Stephan Hasler, Lydia Fischer, Jörg Deigmöller , "A Knowledge-based Multi-entity and Cooperative System Architecture", ICHMS 2020: 1st IEEE International Conference on Human-Machine Systems, 2020.
AbstractFuture intelligent systems will become more complex and they will be composed of potential very different artificial agents like mobile and static robots, and smartphones that collect …data and that have capabilities to perform certain tasks. We rely on the assumption that intelligent devices and humans have at least partially disjoint capabilities. Therefore it can be beneficial to combine intelligent devices and humans in order to perform a ...
Download Bibtex file
Elena Raponi, Hao Wang, Mariusz Bujny, Simonetta Boria, Carola Doerr , "High Dimensional Bayesian Optimization assisted by Principal Component Analysis", International Conference on Parallel Problem Solving From Nature (PPSN), 2020.
AbstractBayesian Optimization (BO) is a surrogate-assisted global optimization technique that has been successfully applied in various fields, e.g., automated machine learning and design optimization. Built upon the so-called infill-criterion and Gaussian Process regression (GPR), the BO technique suffers from a substantial computational complexity and hampered convergence rate as the dimension of the search spaces increases. Scaling up BO for high-dimen...
Download Bibtex file Per Mail Request
David Guirguis, Nikola Aulig, Renato Picelli, Bo Zhu, Yuqing Zhou, William Vicente, Francesco Iorio, Markus Olhofer, Wojciech Matusik, Carlos A. Coello Coello, Kazuhiro Saitou , "Evolutionary Black-Box Topology Optimization: Challenges and Promises", IEEE Transactions on Evolutionary Computation, vol. 24, no. 4, 2020.
AbstractBlack-box topology optimization (BBTO) aims at utilization of evolutionary computation and other soft computing methodologies to generate near-optimal topologies of mechanical structures. Although evolutionary computation is capable to overcome some limitations of conventional and well-received gradient optimization, methods based on BBTO have been criticized for numerous drawbacks. In this paper, we discuss topology optimization as a black-box ...
Download Bibtex file
1 ... 63 64 65 66 67 68 ... 183
Follow us
